A main caveat in current statistical studies of galaxies at z ~ 1 is that the way in which the physical properties of galaxies, such as stellar mass (M∗) and star formation rate (SFR), are generally derived from multi-wavelength datasets does not reflect recent advances in the modeling of galaxy spectral energy distributions (SEDs). For example, spectral analyses often rely on oversimplified modeling of the stellar spectral continuum using simple star formation histories (SFHs), such as exponentially declining τ-models. Some studies have shown that more sophisticated SFH parametrizations provide better agreement with the data (e.g. Lee et al. 2010, 2014; Pacifici et al. 2013; Behroozi et al. 2013). The inclusion of nebular emission is also important to interpret observed SEDs of galaxies. Elaborate prescriptions have been proposed, based on combinations of stellar population synthesis and photoionization codes. We have investigated, in a systematic way, how different SED modeling approaches affect the constraints derived on the physical parameters of high-redshift galaxies.

We used version 4.1 of the 3D-HST Survey photometric catalogue for the GOODS-South field covering an area of 171 arcmin2 (Skelton et al. , 2014). We compiled a sample of 1048 galaxies at redshifts 0.7 < z < 2.8 (H < 23) with accurate photometry at rest-frame UV to near-IR wavelengths (U, ACS-F435W, ACS-F606W, ACS-F775W, ACS-F850lp, WFC3-F125W, WFC3-F140W, WFC3-F160W and IRAC 3.6μm). Grism (low-resolution) observations provided us with reliable spectroscopic redshifts (Brammer et al. 2012) for all galaxies and good optical emission-line equivalent widths (EW) for a subsample of 364 galaxies.

We considered three modeling approaches relying on different assumptions: the explored (prior) ranges of star formation and chemical enrichment histories; attenuation by dust; and nebular emission. We built:

The same P12nEL spectral library, only including also the nebular component (P12, Pacifici et al. 2012).

In Figure 1, we compare the observer-frame colors of the galaxies in the sample (grey symbols) with the predictions of the three model spectral libraries (colored contours). This figure shows that the CLSC spectral library leaves few observed galaxies with no model counterpart. Thus, SED fits for these galaxies will be biased towards the models that lie at the edge of the spectral library. The P12nEL spectral library can cover reasonably well the bulk of the observations, showing the importance of accounting for more realistic ranges of SFHs and dust properties than included in the CLSC spectral library. Few observed galaxies fall outside the contours of the P12nEL model spectral library, presumably because of the contamination of the WFC3-F160W flux by strong Hα emission. The P12 spectral library allows us to cover reasonably well the entire observed color-color space.

Figure 1: Optical-NIR color-color diagrams comparing the 3D-HST sample (grey symbols; open circles mark objects for which error bars are larger than 0.2 mag) with the three model libraries as labeled on top (contours; the three lines mark 50, 16 and 2 per cent of the maximum density).

We compared the constraints on M∗ and SFR derived for all 1048 galaxies in the sample using the CLSC and P12nEL model spectral libraries to those obtained using the more comprehensive P12 library. We summarize the results in Table 1. The use of simple exponentially declining SFHs (CLSC spectral library) can cause strong biases on both the M∗ (~ 0.1 dex) and the SFR (~ −0.6 dex). Not including the emission lines in the broad-band fluxes (P12nEL) does not strongly affect the estimates of M∗, but can induce an overestimation of the SFR (~ 0.1 dex).

Table 1: 16, 50 and 84 percentiles of the distributions of the differences between best estimates of M∗ and SFR when comparing the constraints obtained with different libraries.

To further quantify how emission lines contaminate observed broad-band fluxes, we recorded, for a subsample of the 3D-HST galaxies (364), the contribution by nebular emission to the WFC3-F140W magnitude of the best-fitting P12 model (as derived when including the constraints from both 9-band photometry and EW measurements from 3D-HST grism data). This is shown in Figure 2 as a function of stellar mass. For galaxies at redshifts 0.8 < z < 1.4, both Hα and [S II] fall in the WFC3-F140W filter. Crosses represent the combined contamination caused by these lines. In the same way, we plotted the combined contamination by Hβ and [OIII] for galaxies in the range 1.5 < z < 2.2 (empty circles). Each galaxy is color-coded according to star formation activity, from low (red) to high (black) specific SFR. For galaxies at 0.8 < z < 1.4, the contamination decreases from ~ 0.1 to ~ 0.02 mag as the stellar mass increases from ~ 109.5 to ~ 1011 solar masses. At higher redshift, where [O III] and Hβ are sampled in the band, the contamination is slightly larger because the SFR is on average larger at higher than at lower redshifts (Noeske et al. 2007).

Figure 2: Contribution by nebular emission to the WFC3-F140W magnitude of the best-fitting (P12) model for a subsample of 3D-HST galaxies (for which good grism observations are available) as a function of stellar mass. Crosses represent galaxies with measured Hα and [S II] (0.8 < z < 1.4), while circles represent galaxies with measured Hβ and [O III] (1.5 < z < 2.2). The points are color-coded according to star formation activity from low (red) to high (black) specific SFR. The contamination of the emission lines in the broad-band WFC3-F140W filter increases from ~ 0.01 to ~ 0.5 mag as the stellar mass decreases.

The results obtained in this work revealed the importance of choosing appropriate spectral models to interpret deep galaxy observations. In particular, the biases introduced by the use of classical spectral libraries to derive estimates of M∗ and SFR can significantly affect the interpretation of standard diagnostic diagrams of galaxy evolution, such as the galaxy stellar-mass function and the main sequence of star-forming galaxies. In this context, the spectral library developed by Pacifici et al. (2012) offers the possibility to interpret these and other fundamental diagnostics on the basis of more realistic, and at the same time more versatile models. This is all the more valuable in that the approach can be straightforwardly tailored to the analysis of any combination of photometric and spectroscopic observations of galaxies at any redshift.

This Month’s Featured Author

Dr. Brian Williams received his B.S. from Florida State University in 2004 and his Ph.D. from North Carolina State University in 2010. He was a NASA Postdoctoral Fellow at NASA Goddard Space Flight Center for three years, after which he worked as a research scientist at NASA GSFC with Universities Space Research Association. He arrived at STScI in February of 2017, and is currently a Support Scientist in the Science Mission Office. His research interests include supernovae and supernova remnants, shock physics and particle acceleration, and dust in the interstellar medium.